Abstract
In previous chapters, we have seen that it is possible to resolve dosimetric and radiation protection problems with fairly easy analytical approaches. By the way, some of these logics are implemented in deterministic codes (e.g. point kernel method). However, according to circumstances, these methods can lead to significant bias on results especially for complex radiological cases. In order to offset these issues, it may be necessary to switch to a Monte Carlo method. Computational algorithms based on this method rely on repeated random sampling to obtain numerical results. It allows a smooth definition of physical parameters during the particle transport for more accuracy in final results. Furthermore, this method is particularly suitable in multi-particles transport problems and for complex geometries involving multi-materials and various densities. In what follows, the Monte-Carlo method applied to particle transport is detailed in terms of implementation in codes and features for radiation protection and dosimetric problems. The use of computer codes to estimate the various radiometric and dosimetric quantities previously defined in this book has become essential. These operate using methods of numerical calculations with approximations that affect more or less the true value of desired quantities. We chose to detail a particular, which is the reference method for the simulations in the field of dosimetry and radiation protection, and whose evocation staked all previous chapters: the Monte Carlo method.
Keywords
- MCNP Code
- Continuous Slowing-down Approximation (CSDA)
- Cumulative Probability Density
- Dose Point Kernel (DPK)
- EGSnrc
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Antoni, R., Bourgois, L. (2017). Principle of the Monte-Carlo Method Applied to Dosimetry and Radiation Protection. In: Applied Physics of External Radiation Exposure. Biological and Medical Physics, Biomedical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-48660-4_6
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